[Remote] Graduate Intern – Machine Learning - Solar Forecasting

Note: The job is a remote job and is open to candidates in USA. The National Laboratory of the Rockies (NLR) is a leading institution in energy systems research and development, and they are seeking a Graduate Intern for their Machine Learning - Solar Forecasting team. This role involves developing and implementing AI algorithms for real-time solar forecasting, working closely with a multidisciplinary team to innovate in energy forecasting solutions. Responsibilities Innovate and Optimize: Build best-in-class models for inverter-level and plant-level solar forecasting with calibrated uncertainty, using RNN, diffusion models, and graph models Implement and Impact: Bring your algorithms to life for industry partners, making tangible improvements in solar forecasting Lead and Collaborate: Manage our project GitHub repository for experiment tracking and code versioning, ensuring seamless collaboration with partners and code excellence Share Your Discoveries: Present your groundbreaking results and key findings at workshops, conferences, and in high-quality journals, positioning yourself as a thought leader in the field Skills Minimum of a 3.0 cumulative grade point average Undergraduate: Must be enrolled as a full-time student in a bachelor's degree program from an accredited institution Post Undergraduate: Earned a bachelor's degree within the past 12 months. Eligible for an internship period of up to one year Graduate: Must be enrolled as a full-time student in a master's degree program from an accredited institution Post Graduate: Earned a master's degree within the past 12 months. Eligible for an internship period of up to one year Graduate + PhD: Completed master's degree and enrolled as PhD student from an accredited institution Completed a Bachelor's degree and either have completed a master's degree or be enrolled in a masters or PhD degree in in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related analytical domain Demonstrated knowledge and experience in Python and its related libraries, such as TensorFlow, Keras, and Pytorch Demonstrated experience in time series forecasting, computer vision, and scenario generation A comprehensive understanding of uncertainty quantification Demonstrated experience documenting and presenting results in presentations, papers, and or publications Hands-on experience in energy related time series forecasting, such as participating in energy forecasting competitions Experience in multi-modal machine learning Knowledge about PV plants, PV inverters, and PV control A track record of producing high quality research papers Benefits Medical, dental, and vision insurance 403(b) Employee Savings Plan with employer match Sick leave (where required by law) Performance-, merit-, and achievement- based awards that include a monetary component Relocation expense reimbursement Company Overview The U.S. Department of Energy's primary national laboratory for energy systems research and development. It was founded in 1977, and is headquartered in Golden, Colorado, USA, with a workforce of 1001-5000 employees. Its website is